Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Standard

Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. / Paananen, Topi; Piironen, Juho; Andersen, Michael; Vehtari, Aki.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. 2019. (Proceedings of Machine Learning Research; Vol. 89).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Paananen, T, Piironen, J, Andersen, M & Vehtari, A 2019, Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. in Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 89, International Conference on Artificial Intelligence and Statistics, Naha, Japan, 16/04/2019.

APA

Paananen, T., Piironen, J., Andersen, M., & Vehtari, A. (2019). Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research; Vol. 89).

Vancouver

Paananen T, Piironen J, Andersen M, Vehtari A. Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. 2019. (Proceedings of Machine Learning Research).

Author

Paananen, Topi ; Piironen, Juho ; Andersen, Michael ; Vehtari, Aki. / Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. 2019. (Proceedings of Machine Learning Research).

Bibtex - Download

@inproceedings{b1e288949b2142af842847b6293a277f,
title = "Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution",
abstract = "Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse lengthscale parameter of each input variable as a proxy for variable relevance. This implicitly determined relevance has several drawbacks that prevent the selection of optimal inputvariables in terms of predictive performance. To improve on this, we propose two novel variable selection methods for Gaussian process models that utilize the predictions of a full model in the vicinity of the training points and thereby rank the variables based on their predictive relevance. Our empirical resultson synthetic and real world data sets demonstrate improved variable selection compared to automatic relevance determination in terms of variability and predictive performance.",
author = "Topi Paananen and Juho Piironen and Michael Andersen and Aki Vehtari",
year = "2019",
month = "4",
day = "16",
language = "English",
series = "Proceedings of Machine Learning Research",
publisher = "PMLR",
booktitle = "Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics",

}

RIS - Download

TY - GEN

T1 - Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution

AU - Paananen, Topi

AU - Piironen, Juho

AU - Andersen, Michael

AU - Vehtari, Aki

PY - 2019/4/16

Y1 - 2019/4/16

N2 - Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse lengthscale parameter of each input variable as a proxy for variable relevance. This implicitly determined relevance has several drawbacks that prevent the selection of optimal inputvariables in terms of predictive performance. To improve on this, we propose two novel variable selection methods for Gaussian process models that utilize the predictions of a full model in the vicinity of the training points and thereby rank the variables based on their predictive relevance. Our empirical resultson synthetic and real world data sets demonstrate improved variable selection compared to automatic relevance determination in terms of variability and predictive performance.

AB - Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse lengthscale parameter of each input variable as a proxy for variable relevance. This implicitly determined relevance has several drawbacks that prevent the selection of optimal inputvariables in terms of predictive performance. To improve on this, we propose two novel variable selection methods for Gaussian process models that utilize the predictions of a full model in the vicinity of the training points and thereby rank the variables based on their predictive relevance. Our empirical resultson synthetic and real world data sets demonstrate improved variable selection compared to automatic relevance determination in terms of variability and predictive performance.

UR - https://arxiv.org/abs/1712.08048

UR - https://github.com/topipa/gp-varsel-kl-var

M3 - Conference contribution

T3 - Proceedings of Machine Learning Research

BT - Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics

ER -

ID: 29224560